In recent years, new energy devices such as a Photovoltaic (PV) unit, a battery and a fuel cell (FC) are being introduced in houses. Technologies for HEMS (Home Energy Management Systems) or energy management apparatuses have also extensively been developed. Using technologies of this type enables to implement energy use optimization, energy saving, and cost reduction in customers such as houses.
Many energy management apparatuses estimate an amount of energy to be consumed by energy consuming devices and creates the operation schedules of home appliances, energy storage devices, energy generation devices, and the like (to be generically referred to as electrical devices hereinafter) based on the estimation result.
The energy management apparatus creates the operation schedule of an electrical device based on the estimated value of load electric energy (to be referred to as an estimated load hereinafter). Hence, if the estimation is wrong, it is impossible to create an appropriate operation schedule. For example, assume that a user has two life patterns: a pattern in which he/she goes out during the daytime, and a pattern in which he/she stays at home during the daytime. If an algorithm for the going-out pattern is used to create the operation schedule of an electrical device in the user's home, the estimated load for the at-home case is not accurate. Hence, the operation schedule is not appropriate. If a load estimation algorithm for the at-home pattern is used, the operation schedule in the going-out case is not appropriate.
The estimated loads in both patterns may be added with weighting coefficients such as at-home and going-out frequencies and averaged. However, an operation schedule created based on such an estimated load is eventually inappropriate for both the at-home pattern and the going-out pattern.